Eva Brombacher

The paper introduces an extended RTF method for modelling dose-dependent kinetics in cellular signalling, demonstrating its effectiveness using inflammasome activation data.

The paper introduces an R package for modelling time course data using the retarded transient function approach, enabling analysis of experimental and ODE model trajectories.

This study evaluates deep generative models for integrating single-cell multi-omics data across varying sample sizes and provides recommendations for experimental design.

DIMA selects an imputation algorithm based on data characteristics, achieving reliable performance across proteomics datasets with missing values.

This paper introduces tail-robust quantile normalization to address systematic variance in proteomics data, improving statistical inference by preserving tail features and handling missing values.